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The main statements and inferences of the Dynamic Theory Information (DTI) are considered. It is shown that DTI provides the possibility two reveal two essentially important types of information: objective (unconventional) and subjective (conventional) informtion. There are two ways of obtaining information: reception (perception of an already existing one) and generation (production of new) information. It is shown that the processes of generation and perception of information should proceed in two different subsystems of the same cognitive system. The main points of the Natural-Constructivist Approach to modeling the cognitive process are discussed. It is shown that any neuromorphic approach faces the problem of Explanatory Gap between the “Brain” and the “Mind”, i. e. the gap between objectively measurable information about the ensemble of neurons (“Brain”) and subjective information about the human consciousness (“Mind”). The Natural-Constructive Cognitive Architecture developed within the framework of this approach is discussed. It is a complex block-hierarchical combination of several neuroprocessors. The main constructive feature of this architecture is splitting the whole system into two linked subsystems, by analogy with the hemispheres of the human brain. One of the subsystems is processing the new information, learning, and creativity, i.e. for the generation of information. Another subsystem is responsible for processing already existing information, i.e. reception of information. It is shown that the lowest (zero) level of the hierarchy is represented by processors that should record images of real objects (distributed memory) as a response to sensory signals, which is objective information (and refers to the “Brain”). The next hierarchy levels are represented by processors containing symbols of the recorded images. It is shown that symbols represent subjective (conventional) information created by the system itself and providing its individuality. The highest hierarchy levels containing the symbols of abstract concepts provide the possibility to interpret the concepts of “consciousness”, “sub-consciousness”, “intuition”, referring to the field of “Mind”, in terms of the ensemble of neurons. Thus, DTI provides an opportunity to build a model that allows us to trace how the “Mind” could emerge basing on the “Brain”.

The article considers a mathematical model of decomposition of a complex product into assembly units. This is an important engineering problem, which affects the organization of discrete production and its operational management. A review of modern approaches to mathematical modeling and automated computer-aided of decompositions is given. In them, graphs, networks, matrices, etc. serve as mathematical models of structures of technical systems. These models describe the mechanical structure as a binary relation on a set of system elements. The geometrical coordination and integrity of machines and mechanical devices during the manufacturing process is achieved by means of basing. In general, basing can be performed on several elements simultaneously. Therefore, it represents a variable arity relation, which can not be correctly described in terms of binary mathematical structures. A new hypergraph model of mechanical structure of technical system is described. This model allows to give an adequate formalization of assembly operations and processes. Assembly operations which are carried out by two working bodies and consist in realization of mechanical connections are considered. Such operations are called coherent and sequential. This is the prevailing type of operations in modern industrial practice. It is shown that the mathematical description of such operation is normal contraction of an edge of the hypergraph. A sequence of contractions transforming the hypergraph into a point is a mathematical model of the assembly process. Two important theorems on the properties of contractible hypergraphs and their subgraphs proved by the author are presented. The concept of $s$-hypergraphs is introduced. $S$-hypergraphs are the correct mathematical models of mechanical structures of any assembled technical systems. Decomposition of a product into assembly units is defined as cutting of an $s$-hypergraph into $s$-subgraphs. The cutting problem is described in terms of discrete mathematical programming. Mathematical models of structural, topological and technological constraints are obtained. The objective functions are proposed that formalize the optimal choice of design solutions in various situations. The developed mathematical model of product decomposition is flexible and open. It allows for extensions that take into account the characteristics of the product and its production.

An isolated system, which possesses a discrete set of microscopic states, is considered. The system performs spontaneous random transitions between the microstates. Kinetic equations for the probabilities of the system staying in various microstates are formulated. A general dimensionless expression for entropy of such a system, which depends on the probability distribution, is considered. Two problems are stated: 1) to study the effect of possible unequal probabilities of different microstates, in particular, when the system is in its internal equilibrium, on the system entropy value, and 2) to study the kinetics of microstate probability distribution and entropy evolution of the system in nonequilibrium states. The kinetics for the rates of transitions between the microstates is assumed to be first-order. Two variants of the effects of possible nonequiprobability of the microstates are considered: i) the microstates form two subgroups the probabilities of which are similar within each subgroup but differ between the subgroups, and ii) the microstate probabilities vary arbitrarily around the point at which they are all equal. It is found that, under a fixed total number of microstates, the deviations of entropy from the value corresponding to the equiprobable microstate distribution are extremely small. The latter is a rigorous substantiation of the known hypothesis about the equiprobability of microstates under the thermodynamic equilibrium. On the other hand, based on several characteristic examples, it is shown that the structure of random transitions between the microstates exerts a considerable effect on the rate and mode of the establishment of the system internal equilibrium, on entropy time dependence and expression of the entropy production rate. Under definite schemes of these transitions, there are possibilities of fast and slow components in the transients and of the existence of transients in the form of damped oscillations. The condition of universality and stability of equilibrium microstate distribution is that for any pair of microstates, a sequence of transitions should exist, which provides the passage from one microstate to next, and, consequently, any microstate traps should be absent.

We derive a new model of circadian oscillations in Neurospora crassa, which is suitable to analyze both temporal and spatial dynamics of proteins responsible for mechanism of rythms. The model is based on the non-linear interplay between proteins FRQ and WCC which are products of transcription of frequency and white collar genes forming a feedback loop comprised both positive and negative elements. The main component of oscillations mechanism is supposed to be time-delay in biochemical reactions of transcription. We show that the model accounts for various features observed in Neurospora’s experiments such as entrainment by light cycles, phase shift under light pulse, robustness to action of fluctuations and so on. Wave patterns excited during spatial development of the system are studied. It is shown that the wave of synchronization of biorythms arises under basal transcription factors.

The paper presents a mathematical model that describes the process of obtaining biogas from livestock waste. This model describes the processes occurring in a biogas plant for mesophilic and thermophilic media, as well as for continuous and periodic modes of substrate inflow. The values of the coefficients of this model found earlier for the periodic mode, obtained by solving the problem of model identification from experimental data using a genetic algorithm, are given.

For the model of methanogenesis, an optimal control problem is formulated in the form of a Lagrange problem, whose criterial functionality is the output of biogas over a certain period of time. The controlling parameter of the task is the rate of substrate entry into the biogas plant. An algorithm for solving this problem is proposed, based on the numerical implementation of the Pontryagin maximum principle. In this case, a hybrid genetic algorithm with an additional search in the vicinity of the best solution using the method of conjugate gradients was used as an optimization method. This numerical method for solving an optimal control problem is universal and applicable to a wide class of mathematical models.

In the course of the study, various modes of submission of the substrate to the digesters, temperature environments and types of raw materials were analyzed. It is shown that the rate of biogas production in the continuous feed mode is 1.4–1.9 times higher in the mesophilic medium (1.9–3.2 in the thermophilic medium) than in the periodic mode over the period of complete fermentation, which is associated with a higher feed rate of the substrate and a greater concentration of nutrients in the substrate. However, the yield of biogas during the period of complete fermentation with a periodic mode is twice as high as the output over the period of a complete change of the substrate in the methane tank at a continuous mode, which means incomplete processing of the substrate in the second case. The rate of biogas formation for a thermophilic medium in continuous mode and the optimal rate of supply of raw materials is three times higher than for a mesophilic medium. Comparison of biogas output for various types of raw materials shows that the highest biogas output is observed for waste poultry farms, the least — for cattle farms waste, which is associated with the nutrient content in a unit of substrate of each type.

The article deals with the problem of a distributed system operation reliability. The system core is an open integration platform that provides interaction of varied software for modeling gas transportation. Some of them provide an access through thin clients on the cloud technology “software as a service”. Mathematical models of operation, transmission and computing are to ensure the operation of an automated dispatching system for oil and gas transportation. The paper presents a system solution based on the theory of Markov random processes and considers the stable operation stage. The stationary operation mode of the Markov chain with continuous time and discrete states is described by a system of Chapman–Kolmogorov equations with respect to the average numbers (mathematical expectations) of the objects in certain states. The objects of research are both system elements that are present in a large number – thin clients and computing modules, and individual ones – a server, a network manager (message broker). Together, they are interacting Markov random processes. The interaction is determined by the fact that the transition probabilities in one group of elements depend on the average numbers of other elements groups.

The authors propose a multi-criteria dispersion model of risk assessment for such systems (both in the broad and narrow sense, in accordance with the IEC standard). The risk is the standard deviation of estimated object parameter from its average value. The dispersion risk model makes possible to define optimality criteria and whole system functioning risks. In particular, for a thin client, the following is calculated: the loss profit risk, the total risk of losses due to non-productive element states, and the total risk of all system states losses.

Finally the paper proposes compromise schemes for solving the multi-criteria problem of choosing the optimal operation strategy based on the selected set of compromise criteria.

In this work, we develop a model of the immune response to respiratory viral infections taking into account some particular properties of the SARS-CoV-2 infection. The model represents a system of ordinary differential equations for the concentrations of epithelial cells, immune cells, virus and inflammatory cytokines. Conventional analysis of the existence and stability of stationary points is completed by numerical simulations in order to study dynamics of solutions. Behavior of solutions is characterized by large peaks of virus concentration specific for acute respiratory viral infections.

At the first stage, we study the innate immune response based on the protective properties of interferon secreted by virus-infected cells. On the other hand, viral infection down-regulates interferon production. Their competition can lead to the bistability of the system with different regimes of infection progression with high or low intensity. In the case of infection outbreak, the incubation period and the maximal viral load depend on the initial viral load and the parameters of the immune response. In particular, increase of the initial viral load leads to shorter incubation period and higher maximal viral load.

In order to study the emergence and dynamics of cytokine storm, we consider proinflammatory cytokines produced by cells of the innate immune response. Depending on parameters of the model, the system can remain in the normal inflammatory state specific for viral infections or, due to positive feedback between inflammation and immune cells, pass to cytokine storm characterized by excessive production of proinflammatory cytokines. Furthermore, inflammatory cell death can stimulate transition to cytokine storm. However, it cannot sustain it by itself without the innate immune response. Assumptions of the model and obtained results are in qualitative agreement with the experimental and clinical data.

We have investigated dynamics of synthetic genetic oscillators — repressilators — coupled through autoinducer diffusion. The model of the system with phase-repulsive coupling structure is under consideration. We have examined emergence of periodic regimes, stable inhomogeneous steady states depending on the main systems’ parameters: coupling strength and maximal transcription rate. It has been shown that autoinducer production module added to the isolated repressilator cause the limit cycle to disappear through infinite period bifurcation for sufficiently large transcription rate. We have found hysteresis of limit cycle and stable steady state the size of which is determined by ratio between mRNA and protein lifetimes. Two coupled oscillators system demonstrates stable anti-phase oscillations which can become a chaotic regime through invariant torus emergence or via Feigenbaum scenario.

In case of vessel wall damage or contact of blood plasma with a foreign surface, the chain of chemical reactions called coagulation cascade is launched that leading to the formation of a fibrin clot. A key enzyme of the coagulation cascade is thrombin, which catalyzes formation of fibrin from fibrinogen. The distribution of thrombin concentration in blood plasma determines spatio-temporal dynamics of clot formation. Contact pathway of blood coagulation triggers the production of thrombin in response to the contact with a negatively charged surface. If the concentration of thrombin generated at this stage is large enough, further production of thrombin takes place due to positive feedback loops of the coagulation cascade. As a result, thrombin propagates in plasma cleaving fibrinogen that results in the clot formation. The concentration profile and the speed of propagation of thrombin are constant and do not depend on the type of the initial activator.

Such behavior of the coagulation system is well described by the traveling wave solutions in a system of “reaction – diffusion” equations on the concentration of blood factors involved in the coagulation cascade. In this study, we carried out detailed analysis of the mathematical model describing the main reaction of the intrinsic pathway of coagulation cascade.We formulate necessary and sufficient conditions of the existence of the traveling wave solutions. For the considered model the existence of such solutions is equivalent to the existence of the wave solutions in the simplified one-equation model describing the dynamics of thrombin concentration derived under the quasi-stationary approximation.

Simplified model also allows us to obtain analytical estimate of the thrombin propagation rate in the considered model. The speed of the traveling wave for one equation is estimated using the narrow reaction zone method and piecewise linear approximation. The resulting formulas give a good approximation of the velocity of propagation of thrombin in the simplified, as well as in the original model.

This article solves the problem of constructing a neuro-fuzzy model of fuzzy rules formation and using them for objects state evaluation in conditions of uncertainty. Traditional mathematical statistics or simulation modeling methods do not allow building adequate models of objects in the specified conditions. Therefore, at present, the solution of many problems is based on the use of intelligent modeling technologies applying fuzzy logic methods. The traditional approach of fuzzy systems construction is associated with an expert attraction need to formulate fuzzy rules and specify the membership functions used in them. To eliminate this drawback, the automation of fuzzy rules formation, based on the machine learning methods and algorithms, is relevant. One of the approaches to solve this problem is to build a fuzzy neural network and train it on the data characterizing the object under study. This approach implementation required fuzzy rules type choice, taking into account the processed data specificity. In addition, it required logical inference algorithm development on the rules of the selected type. The algorithm steps determine the number and functionality of layers in the fuzzy neural network structure. The fuzzy neural network training algorithm developed. After network training the formation fuzzyproduction rules system is carried out. Based on developed mathematical tool, a software package has been implemented. On its basis, studies to assess the classifying ability of the fuzzy rules being formed have been conducted using the data analysis example from the UCI Machine Learning Repository. The research results showed that the formed fuzzy rules classifying ability is not inferior in accuracy to other classification methods. In addition, the logic inference algorithm on fuzzy rules allows successful classification in the absence of a part of the initial data. In order to test, to solve the problem of assessing oil industry water lines state fuzzy rules were generated. Based on the 303 water lines initial data, the base of 342 fuzzy rules was formed. Their practical approbation has shown high efficiency in solving the problem.